Detecting sleep to evaluate therapy

ABSTRACT

A system includes one or more sensors and a processor. Each of the sensors generates a signal as a function of at least one physiological parameter of a patient that may discernibly change when the patient is asleep. The processor monitors the physiological parameters, and determines whether the patient is asleep based on the parameters. In some embodiments, the processor determines plurality of sleep metric values, each of which indicates a probability of the patient being asleep, based on each of a plurality of physiological parameters. The processor may average or otherwise combine the plurality of sleep metric values to provide an overall sleep metric value that is compared to a threshold value in order to determine whether the patient is asleep. In addition, an electroencephalogram signal may be used to identify sleep states of the patient.

This application is a continuation-in-part of U.S. application Ser. No.11/081,786, filed Mar. 16, 2005, which is a continuation-in-part of U.S.application Ser. No. 10/825,964, filed Apr. 15, 2004, which claims thebenefit of U.S. provisional application No. 60/553,771, filed Mar. 16,2004. This application also claims the benefit of U.S. ProvisionalApplication No. 60/785,822, filed Mar. 24, 2006. The entire content ofeach of these applications is incorporated herein by reference.

TECHNICAL FIELD

The invention relates to medical devices, and to techniques fordetermining whether a patient is asleep.

BACKGROUND

The ability to determine whether a patient is asleep is useful in avariety of medical contexts. In some situations, the ability todetermine whether a patient is asleep is used to diagnose conditions ofthe patient. For example, the amount of time that patients sleep, theextent of arousals during sleep, and the times of day that patientssleep have been used to diagnose sleep apnea. Such sleep informationcould also be used to diagnose psychological disorders, such asdepression, mania, bipolar disorder, or obsessive-compulsive disorder.

In other situations, a determination as to whether a patient is asleepis used to control delivery of therapy to the patient. For example,neurostimulation or drug therapies can be suspended when the patient isasleep, or the intensity/dosage of the therapies can be reduced when apatient is asleep. As another example, the rate response settings of acardiac pacemaker may be adjusted to less aggressive settings when thepatient is asleep so that the patient's heart will not be paced at aninappropriately high rate during sleep. In these examples, therapy maybe suspended or adjusted when the patient is asleep to avoid patientdiscomfort, or to conserve a battery and/or contents of a fluidreservoir of an implantable medical device when the therapy may beunneeded or ineffective. However, in other cases, a therapy intended tobe delivered when the patient is asleep, such as therapy intended toprevent or treat sleep apnea, is delivered based on a determination thatthe patient is asleep. Other ailments that may negatively affect patientsleep quality include movement disorders, such as tremor, Parkinson'sdisease, multiple sclerosis, epilepsy, or spasticity, as well as sleepapnea, congestive heart failure, gastrointestinal disorders andincontinence. All of these disorders may be generally classified asneurological disorders.

Existing techniques for determining whether a patient is asleep includemonitoring the electroencephalogram (EEG) of the patient to identifybrain wave activity indicative of sleep. However, EEG monitoringtypically requires that an array of electrodes be placed on a patient'sscalp and coupled to an external monitoring device, and is most oftenperformed in a clinic setting. Generally, an implantable medical devicemay only be used to monitor a patient's EEG in the rare cases when it iscoupled to electrodes implanted within the brain of the patient.Consequently, existing EEG monitoring techniques are generallyunsuitable for determining whether a patient is asleep in order tocontrol therapy, or for long-term monitoring of the patient's sleep/wakecycle.

Existing techniques employed by implantable medical devices to determinewhether a patient is asleep include monitoring the patient's respirationrate, respiration rate variability, and activity level. Each of thesephysiological parameters may be an inaccurate indicator of whether apatient is asleep. For example, from the perspective of thesephysiological parameters, it may appear that a patient is sleeping when,instead, the patient is merely lying down in a relaxed state. As anotherexample, respiration rate and respiration rate variability, for example,may fail to accurately indicate that the patient is asleep when thepatient suffers from a breathing disorder, such as Cheyne-Stokessyndrome.

SUMMARY

In general, the invention is directed to techniques for determiningwhether a patient is asleep. In some embodiments, the invention isdirected to techniques that involve determination of values of one ormore sleep metrics that indicate a probability of a patient being asleepbased on the current value of one or more physiological parameters ofthe patient. Use of a plurality of sleep metrics, in particular, mayallow for a more accurate determination of whether a patient is asleep.

A system according to the invention includes one or more sensors and aprocessor. Each of the sensors generates a signal as a function of atleast one physiological parameter of a patient that may discerniblychange when the patient is asleep. Exemplary physiological parametersinclude activity level, posture, heart rate, electrocardiogram (ECG)morphology, respiration rate, respiratory volume, blood pressure, bloodoxygen saturation, partial pressure of oxygen within blood, partialpressure of oxygen within cerebrospinal fluid, muscular activity andtone, core temperature, subcutaneous temperature, arterial blood flow,brain electrical activity, eye motion, and galvanic skin response.

The processor monitors the physiological parameters based on the signalsgenerated by the sensors, and determines whether the patient is asleepbased on values for the physiological parameters. The value for aphysiological parameter may be a current, mean or median value for theparameter. In some embodiments, the processor may additionally oralternatively determine whether the patient is asleep based on thevariability of one or more of the physiological parameters.

In some embodiments, the processor determines a value of a sleep metricthat indicates a probability of the patient being asleep based on aphysiological parameter. In particular, the processor may apply afunction or look-up table to the current value and/or variability of thephysiological parameter to determine the sleep metric value. Theprocessor may compare the sleep metric value to a threshold value todetermine whether the patient is asleep. In some embodiments, theprocessor may compare the sleep metric value to each of a plurality ofthresholds to determine the current sleep state of the patient, e.g.,rapid eye movement (REM), or one of the nonrapid eye movement (NREM)states (S1, S2, S3, S4). Because they provide the most “refreshing” typeof sleep, the ability to determine whether the patient is in one of theS3 and S4 sleep states may be, in some embodiments, particularly useful.

Further, in some embodiments the processor may determine a sleep metricvalue for each of a plurality of physiological parameters. In otherwords, the processor may apply a function or look-up table for eachparameter to the current value for that parameter in order to determinethe sleep metric value for that parameter. The processor may average orotherwise combine the plurality of sleep metric values to provide anoverall sleep metric value for comparison to the threshold values. Insome embodiments, a weighting factor may be applied to one or more ofthe sleep metric values. One or more of functions, look-up tables,thresholds and weighting factors may be selected or adjusted by a userin order to select or adjust the sensitivity and specificity of thesystem in determining whether the patient is asleep.

In some embodiments, the processor may determine whether the patient isasleep, at least in part, by analyzing an electroencephalogram (EEG) ofthe patient. For example, the processor may determine whether thepatient is asleep based on the frequency, e.g., predominant frequency,in the EEG. Further, the processor may determine in which sleep state(S1-S4 and REM) the patient is based on what frequency or range offrequencies are evident in the EEG.

In some embodiments, the processor is included as part of a medicaldevice, such as an implantable medical device. The sensors may also beincluded within the medical device, coupled to the medical device by oneor more leads, or in wireless communication with the medical device. Themedical device may control delivery of therapy to the patient based onthe determination as to whether the patient is asleep, or may storeinformation indicating when the patient is asleep for later retrievaland analysis by user. In some embodiments, the medical device mayinstead use the one or more sleep metric values to control delivery oftherapy, or may store one or more sleep metric values. In someembodiments, information relating to the patient's sleep patterns may beused to diagnose sleep disorders, chronic pain, and neurologicaldisorders that include movement and psychological disorders. Exampledisorders may include Parkinson's disease, tremor, multiple sclerosis,spasticity, or epilepsy. Information relating to the patient's sleeppatterns may also be used to diagnose cardiac disorders such ascongestive heart failure or arrhythmia, or psychological disorders suchas depression, mania, bipolar disorder, or obsessive-compulsivedisorder. Further, information relating to a patient's sleep patternsmay be used to evaluate the effectiveness of a therapy delivered to thepatient to treat any of these ailments or symptoms.

In one embodiment, the invention is directed to a method for evaluatingthe efficacy of at least one of a movement disorder therapy,psychological disorder therapy, or deep brain stimulation which includesmonitoring at least physiological parameter of a patient via animplantable medical device that delivers the at least one of themovement disorder therapy, psychological disorder therapy, or deep brainstimulation to the patient, monitoring sleep patterns of the patientwith the implantable medical device based on the physiologicalparameter, and presenting sleep quality information to a user based onthe sleep patterns for evaluation of the efficacy of the at least one ofthe movement disorder therapy, psychological disorder therapy, or deepbrain stimulation.

In another embodiment, the invention is directed to a medical systemthat includes a sensor that generates a signal as a function of at leastone physiological parameter of a patient, and an implantable medicaldevice that delivers at least one of a movement disorder therapy,psychological disorder therapy, or deep brain stimulation, monitors theat least one physiological parameter of the patient based on the signaloutput by the sensor, and monitors sleep patterns of the patient basedon the physiological parameter. The system further comprises a computingdevice that provides sleep quality information based on the sleeppatterns for evaluation of the efficacy of the at least one of themovement disorder therapy, psychological disorder therapy, or deep brainstimulation.

In an additional embodiment, the invention is directed to a system thatincludes means for monitoring at least physiological parameter of apatient via an implantable medical device that delivers the at least oneof the movement disorder therapy, psychological disorder therapy, ordeep brain stimulation to the patient, means for monitoring sleeppatterns of the patient with the implantable medical device based on thephysiological parameter, and means for presenting sleep qualityinformation to a user based on the sleep patterns for evaluation of theefficacy of the at least one of the movement disorder therapy,psychological disorder therapy, or deep brain stimulation.

The invention may be capable of providing one or more advantages. Forexample, the invention provides techniques for determining a sleep stateof a patient that may be implemented in an implantable medical device.Further, the techniques provided by the invention may include analysisof a variety of physiological parameters not previously used indetermining whether a patient is asleep. Where it is desired to detectsleep via an implantable medical device, the ability to determinewhether a patient is sleeping based on these physiological parametersmay increase the number of implantable medical device types in which theinvention may be implemented, i.e., the invention may be implemented ina variety of types of implantable medical devices which include or maybe easily modified to include sensors capable of generating a signalbased on such physiological parameters.

Monitoring a plurality of physiological parameters according to someembodiments, rather than a single parameter, may allow for a moreaccurate determination of whether a patient is asleep than is availablevia existing implantable medical devices. Use of sleep metrics thatindicate a probability of the patient being asleep for each of aplurality of physiological parameters may further increase thereliability with which an implantable medical device may determinewhether a patient is asleep. In particular, rather than a binary sleepor awake determination for each of a plurality of parameters, sleepmetric values for each of a plurality of parameters may be combined toyield an overall sleep metric value that may be compared to a thresholdto determine whether the patient is asleep. In other words, failure ofany one physiological parameter to accurately indicate whether a patientis sleeping may be less likely to prevent the implantable medical devicefrom accurately indicating whether the patient is sleeping whenconsidered in combination with other physiological parameters.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIGS. 1A and 1B are conceptual diagrams illustrating example systemsincluding an implantable medical device that determines whether apatient is asleep according to the invention.

FIGS. 2A and 2B are block diagrams further illustrating the examplesystems and implantable medical devices of FIGS. 1A and 1B.

FIG. 3 is a logic diagram illustrating an example circuit that detectsthe sleep state of a patient from the electroencephalogram (EEG) signal.

FIG. 4 is a block diagram illustrating a memory within an implantablemedical device of the system of FIG. 1.

FIG. 5 is a flowchart illustrating an example technique for determiningwhether a patient is asleep.

FIG. 6 is a conceptual diagram illustrating a monitor that monitorsvalues of one or more accelerometers of the patient instead of, or inaddition to, a therapy delivering medical device.

FIG. 7 is a flow diagram illustrating monitoring the heart rate andbreathing rate of a patient by measuring cerebral spinal fluid pressure.

DETAILED DESCRIPTION

FIGS. 1A and 1B are conceptual diagrams illustrating example systems 10Aand 10B (collectively “systems 10”) that respectively include animplantable medical device (IMD) 14A or 14B (collectively “IMDs 14”)that determine whether a respective one of patients 12A and 12B(collectively “patients 12”) is asleep according to the invention. Inthe illustrated example system, IMDs 14 take the form of an implantableneurostimulator that delivers neurostimulation therapy in the form ofelectrical pulses to patients 12. However, the invention is not limitedto implementation via an implantable neurostimulator, or even toimplementation via IMDs.

For example, in some embodiments of the invention, IMDs 14 may take theform of an implantable pump or implantable cardiac pacemaker maydetermine whether a patient is asleep. In other embodiments, the medicaldevice that determines when patients 12 are asleep may be an implantableor external patient monitor. Further, a programming device or othercomputing device may determine when patients 12 is asleep based oninformation collected by a medical device. In other words, anyimplantable or external device may determine whether a patient is asleepaccording to the invention.

In the illustrated example systems 10, IMDs 14 respectively deliverneurostimulation therapy to patients 12A and 12B via leads 16A and 16B,and leads 16C and 16D (collectively “leads 16”), respectively. Leads 16Aand 16B may, as shown in FIG. 1A, be implanted proximate to the spinalcord 18 of patient 12A, and IMD 14A may deliver spinal cord stimulation(SCS) therapy to patient 12A in order to, for example, reduce painexperienced by patient 12A. However, the invention is not limited to theconfiguration of leads 16A and 16B shown in FIG. 1A or the delivery ofSCS or other pain therapies.

For example, in another embodiment, illustrated in FIG. 1B, leads 16Cand 16D may extend to brain 19 of patient 12B, e.g., through cranium 17of patient. IMD 14B may deliver deep brain stimulation (DBS) or corticalstimulation therapy to patient 12 to treat any of a variety ofnon-respiratory neurological disorders, such as movement disorders orpsychological disorders. Example therapies may treat tremor, Parkinson'sdisease, spasticity, epilepsy, depression or obsessive-compulsivedisorder. Non-respiratory neurological disorders exclude respiratorydisorders, such as sleep apnea. As illustrated in FIG. 1B, leads 16C and16D may be coupled to IMD 14B via one or more lead extensions 15.

As further examples, one or more leads 16 may be implanted proximate tothe pelvic nerves (not shown) or stomach (not shown), and an IMD 14 maydeliver neurostimulation therapy to treat incontinence or gastroparesis.Additionally, leads 16 may be implanted on or within the heart to treatany of a variety of cardiac disorders, such as congestive heart failureor arrhythmia, or may be implanted proximate to any peripheral nerves totreat any of a variety of disorders, such as peripheral neuropathy orother types of chronic pain.

The illustrated numbers and locations of leads 16 are merely examples.Embodiments of the invention may include any number of lead implanted atany of a variety of locations within a patient. Furthermore, theillustrated number and location of IMDs 14 are merely examples. IMDs 14may be located anywhere within patient according to various embodimentsof the invention. For example, in some embodiments, an IMD 14 may beimplanted on or within cranium 17 for delivery of therapy to brain 19,or other structure of the head of the patient 12.

IMDs 14 deliver therapy according to a set of therapy parameters thatdefine the delivered therapy. In embodiments where IMDs 14 deliversneurostimulation therapy in the form of electrical pulses, theparameters for each of the parameter sets may include voltage or currentpulse amplitudes, pulse widths, pulse rates, and the like. Further, eachof leads 16 includes electrodes (not shown in FIG. 1), and theparameters may include information identifying which electrodes havebeen selected for delivery of pulses, and the polarities of the selectedelectrodes. In embodiments in which IMDs 14 deliver other types oftherapies, therapy parameter sets may include other therapy parameterssuch as drug concentration and drug flow rate in the case of drugdelivery therapy.

Each of systems 10 may also includes a clinician programmer 20(illustrated as part of system 10A in FIG. 1A). A clinician (not shown)may use clinician programmer 20 to program neurostimulation therapy forpatient 12A. Clinician programmer 20 may, as shown in FIG. 1A, be ahandheld computing device. Clinician programmer 20 includes a display22, such as a LCD or LED display, to display information to a user.Clinician programmer 20 may also include a keypad 24, which may be usedby a user to interact with clinician programmer 20. In some embodiments,display 22 may be a touch screen display, and a user may interact withclinician programmer 20 via display 22. A user may also interact withclinician programmer 20 using peripheral pointing devices, such as astylus, mouse, or the like. Keypad 24 may take the form of analphanumeric keypad or a reduced set of keys associated with particularfunctions.

Systems 10 also includes a patient programmer 26 (illustrated as part ofsystem 10A in FIG. 1A), which also may, as shown in FIG. 1A, be ahandheld computing device. Patient 12A may use patient programmer 26 tocontrol the delivery of neurostimulation therapy by IMD 14A. Patientprogrammer 26 may also include a display 28 and a keypad 30, to allowpatient 12A to interact with patient programmer 26. In some embodiments,display 26 may be a touch screen display, and patient 12A may interactwith patient programmer 26 via display 28. Patient 12A may also interactwith patient programmer 26 using peripheral pointing devices, such as astylus or mouse.

IMDs 14, clinician programmer 20 and patient programmer 26 may, as shownin FIG. 1A, communicate via wireless communication. Clinician programmer20 and patient programmer 26 may, for example, communicate via wirelesscommunication with IMD 14A using RF telemetry techniques known in theart. Clinician programmer 20 and patient programmer 26 may communicatewith each other using any of a variety of local wireless communicationtechniques, such as RF communication according to the 802.11 orBluetooth specification sets, infrared communication according to theIRDA specification set, or other standard or proprietary telemetryprotocols.

Clinician programmer 20 and patient programmer 26 need not communicatewirelessly, however. For example, programmers 20 and 26 may communicatevia a wired connection, such as via a serial communication cable, or viaexchange of removable media, such as magnetic or optical disks, ormemory cards or sticks. Further, clinician programmer 20 may communicatewith one or both of IMD 14A and patient programmer 26 via remotetelemetry techniques known in the art, communicating via a local areanetwork (LAN), wide area network (WAN), public switched telephonenetwork (PSTN), or cellular telephone network, for example.

As mentioned above, IMDs 14 are capable of determining whether patients12 are asleep. Specifically, as will be described in greater detailbelow, IMDs 14 monitor a plurality of physiological parameters ofpatients 12 that may discernibly change when patients 12 are asleep, anddetermines whether patients 12 are asleep based on values of thephysiological parameters. The value for a physiological parameter may bea current, mean or median value for the parameter. In some embodiments,IMDs 14 may additionally or alternatively determine whether a patient 12is asleep based on the variability of one or more of the physiologicalparameters. IMDs 14 include, are coupled to, or are in wirelesscommunication with one or more sensors, and monitor the physiologicalparameters via the sensors.

Exemplary physiological parameters that may be monitored by IMDs 14include activity level, posture, heart rate, electrocardiogram (ECG)morphology, respiration rate, respiratory volume, blood pressure, bloodoxygen saturation, partial pressure of oxygen within blood, partialpressure of oxygen within cerebrospinal fluid, muscular activity andtone, core temperature, subcutaneous temperature, arterial blood flow,brain electrical activity (such as an electroencephalogram or EEG), andeye motion (such as an electro-oculogram or EOG). In some externalmedical device embodiments of the invention, galvanic skin response mayadditionally or alternatively be monitored. Some of the parameters, suchas activity level, heart rate, some ECG morphological features,respiration rate, respiratory volume, blood pressure, arterial oxygensaturation and partial pressure, partial pressure of oxygen in thecerebrospinal fluid, muscular activity and tone, core temperature,subcutaneous temperature, arterial blood flow, and galvanic skinresponse may be at low values when a patient 12 is asleep. Further, thevariability of at least some of these parameters, such as heart rate andrespiration rate, may be at a low value when the patient is asleep.Information regarding the posture of a patient 12 will most likelyindicate that a patient 12 is lying down when a patient 12 is asleep.

In some embodiments, IMDs 14 determine a value of one or more sleepmetrics based on a value of one or more physiological parameters of apatient 12. A sleep metric value may be a numeric value that indicatesthe probability that a patient 12 is asleep. In some embodiments, thesleep metric value may be a probability value, e.g., a number within therange from 0 to 1.

In particular, IMDs 14 may apply a function or look-up table to thecurrent, mean or median value, and/or the variability of thephysiological parameter to determine a value of the sleep metric. IMDs14 may compare the sleep metric value to a threshold value to determinewhether the patient is asleep. In some embodiments, IMDs 14 may comparethe sleep metric value to each of a plurality of thresholds to determinethe current sleep state of the patient, e.g., rapid eye movement (REM),S1, S2, S3, or S4. Because they provide the most “refreshing” type ofsleep, the ability to determine whether the patient is in one of the S3and S4 sleep states may be, in some embodiments, particularly useful.

Further, in some embodiments IMDs 14 may determine a sleep metric valuefor each of a plurality of physiological parameters. In other words,IMDs 14 may apply a function or look-up table for each parameter to avalue for that parameter in order to determine the sleep metric valuefor that parameter. IMDs 14 may average or otherwise combine theplurality of sleep metric values to provide an overall sleep metricvalue for comparison to the threshold values. In some embodiments, IMDs14 may apply a weighting factor to one or more of the sleep metricvalues prior to combination. One or more of functions, look-up tables,thresholds and weighting factors may be selected or adjusted by a user,such as a clinician via programmer 20 or a patient 12 via programmer 26,in order to select or adjust the sensitivity and specificity of IMDs 14in determining whether a patient 12 is asleep.

Monitoring a plurality of physiological parameters according to someembodiments, rather than a single parameter, may allow IMDs 14 todetermine whether a patient 12 is asleep with more accuracy thanexisting implantable medical devices. Use of sleep metric values thatindicate a probability of the patient being asleep for each of aplurality of physiological parameters may further increase the accuracywith which IMDs 14 may determine whether a patient 12 is asleep. Inparticular, rather than a binary sleep or awake determination for eachof a plurality of parameters, sleep metric values for each of aplurality of parameters may be combined to yield an overall sleep metricvalue that may be compared to a threshold to determine whether a patient12 is asleep. In other words, failure of any one physiological parameterto accurately indicate whether a patient is sleeping may be less likelyto prevent IMDs 14 from accurately indicating whether a patient 12 issleeping when considered in combination with other physiologicalparameters.

In some embodiments, the IMDs 14 may determine whether the patient isasleep, at least in part, by analyzing an electroencephalogram (EEG) ofthe patient. For example, the IMDs 14 may determine whether the patientis asleep based on the amplitude or frequency, e.g., predominantfrequency, in the EEG. Further, the IMDs 14 may determine in which sleepstate (S1-S4 and REM) the patient is based on what frequency or range offrequencies are evident in the EEG.

IMDs 14 may control delivery of therapy to a patient 12 based on thedetermination as to whether the patient 12 is asleep. For example, IMDs14 may suspend delivery of neurostimulation or reduce the intensity ofdelivered neurostimulation when a patient 12 is determined to be asleep.In other embodiments, IMDs 14 may suspend or reduce intensity of drugdelivery, or may reduce the aggressiveness of rate response for cardiacpacing when a patient 12 is determined to be asleep. In still otherembodiments, IMDs 14 may initiate delivery of a therapy, such as atherapy to treat or prevent sleep apnea, when a patient 12 is determinedto be asleep.

In some embodiments, IMDs 14 store information indicating when a patient12 is asleep, which may be retrieved for analysis by a clinician viaprogrammer 20, for example. The clinician may use the sleep informationto diagnose conditions of a patient 12, such as sleep apnea orpsychological disorders, such as depression, mania, bipolar disorder, orobsessive-compulsive disorder. Information relating to the sleeppatterns of a patient 12 may in other situations indicate theeffectiveness of a delivered therapy and/or the need for increasedtherapy. Some ailments of a patient 12, such as chronic pain, movementdisorders such as tremor, Parkinson's disease, multiple sclerosis, orspasticity, psychological disorders, gastrointestinal disorders,incontinence, congestive heart failure, and sleep apnea may disturb orhinder the sleep or a patient 12, or, in some cases, inadequate ordisturbed sleep may increase the symptoms of these ailments.

IMDs 14 may collect information relating to the sleep patterns of apatient 12, which may be retrieved by a clinician or patient 12 viaprogrammer 20, 26 and used to evaluate the effectiveness of a therapydelivered to the patient 12 for such an ailment, or to indicate the needfor an additional therapy to improve the sleep pattern of the patient12. In some embodiments, IMDs 14 may determine when a patient isattempting to sleep based on an indication via a user interface of, forexample, a programming device, or monitored physiological parameters.IMDs 14 may also determine when a patient is asleep based on monitoringphysiological parameters as described herein. With such information, theIMDs 14 may determine, as examples, the percentage of time a patient wasasleep when trying to sleep, or sleep efficiency, and the amount of timerequired for the patient to fall asleep, or sleep latency.

Additionally, the IMDs 14 may track the total time sleeping per day,time spent in deeper sleep states, e.g., S3 and S4, or a number ofarousal events during sleep, using the techniques described herein foridentifying whether a patient is asleep and in which sleep state apatient is. Each of these sleep quality metrics may reflect the qualityof sleep experienced by a patient, and thereby indicate theeffectiveness of a therapy or a particular parameter set for thetherapy. The IMDs 14 may associate values for such metrics with thetherapy delivered, or therapy parameter set used to control delivery ofthe therapy, at the time when the value was determined, for the purposeof allowing a user to evaluate the therapies or parameter sets. In somecases, IMDs 14 may evaluate such collected sleep information andautomatically adjust a therapy for such a condition based on theevaluation.

Further information regarding evaluation of a therapy based on sleepinformation collected by an IMD may be found in a commonly-assigned andcopending U.S. patent application Ser. No. ______ by Ken Heruth andKeith Miesel, entitled “COLLECTING SLEEP QUALITY INFORMATION VIA AMEDICAL DEVICE,” which is filed on Mar. 26, 2007 and assigned attorneydocket number 1023-350US03. Further information regarding automaticcontrol of a therapy based on sleep information collected by an IMD maybe found in a commonly-assigned and copending U.S. patent applicationSer. No. ______ by Ken Heruth and Keith Miesel, entitled “CONTROLLINGTHERAPY BASED ON SLEEP QUALITY,” which is filed on Mar. 26, 2007 andassigned attorney docket number 1023-363US03. The entire content of bothof these applications is incorporated herein by reference.

FIGS. 2A and 2B are block diagrams further illustrating systems 10A and10B. In particular, FIG. 2A illustrates an example configuration of IMD14A and leads 16A and 16B. FIG. 2B illustrates an example configurationof IMD 14B and leads 16C and 16D. FIGS. 2A and 2B also illustratesensors 40A and 40B (collectively “sensors 40”) that generate signals asa function of one or more physiological parameters of patients 12. IMDs14 monitor the signals to determine whether patient 12 is asleep.

IMD 14A may deliver neurostimulation therapy via electrodes 42A-D oflead 16A and electrodes 42E-H of lead 16B, while IMD 14B deliversneurostimulation via electrodes 421-L of lead 16C and electrodes 42 M-Pof lead 16D (collectively “electrodes 42”). Electrodes 42 may be ringelectrodes. The configuration, type and number of electrodes 42illustrated in FIGS. 2A and 2B are merely exemplary. For example, leads16 may each include eight electrodes 42, and the electrodes 42 need notbe arranged linearly on each of leads 16.

In each of systems 10A and 10B, electrodes 42 are electrically coupledto a therapy delivery module 44 via leads 16. Therapy delivery module 44may, for example, include a pulse generator coupled to a power sourcesuch as a battery. Therapy delivery module 44 may deliver electricalpulses to a patient 12 via at least some of electrodes 42 under thecontrol of a processor 46, which controls therapy delivery module 44 todeliver neurostimulation therapy according to a set of therapyparameters, which may be one of a plurality of therapy parameter setsstored in memory 48. However, the invention is not limited toimplantable neurostimulator embodiments or even to IMDs that deliverelectrical stimulation. For example, in some embodiments a therapydelivery module 44 of an IMD may include a pump, circuitry to controlthe pump, and a reservoir to store a therapeutic agent for delivery viathe pump.

Processor 46 may include a microprocessor, a controller, a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a field-programmable gate array (FPGA), discrete logiccircuitry, or the like. Memory 48 may include any volatile,non-volatile, magnetic, optical, or electrical media, such as a randomaccess memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM),electrically-erasable programmable ROM (EEPROM), flash memory, and thelike. In some embodiments, memory 48 stores program instructions that,when executed by processor 46, cause IMD 14 and processor 46 to performthe functions attributed to them herein.

Each of sensors 40 generates a signal as a function of one or morephysiological parameters of a patient 12. Although shown as includingtwo sensors 40, systems 10 may include any number of sensors. Asillustrated in FIGS. 2A and 2B, sensors 40 may be included as part ofIMDs 14, or coupled to IMDs 14 via leads 16. Sensors 40 may be coupledto IMDs 14 via therapy leads 16, or via other leads 16, such as lead 16Edepicted in FIGS. 2A and 2B. In some embodiments, a sensor locatedoutside of IMDs 14 may be in wireless communication with processor 46.Wireless communication between sensors 40 and IMDs 14 may, as examples,include RF communication or communication via electrical signalsconducted through the tissue and/or fluid of a patient 12.

As discussed above, exemplary physiological parameters of a patient 12that may be monitored by IMDs 14 to determine values of one or moresleep metrics include activity level, posture, heart rate, ECGmorphology, respiration rate, respiratory volume, blood pressure, bloodoxygen saturation, partial pressure of oxygen within blood, partialpressure of oxygen within cerebrospinal fluid, muscular activity andtone, core temperature, subcutaneous temperature, arterial blood flow,brain electrical activity, and eye motion. Further, as discussed above,in some external medical device embodiments of the invention galvanicskin response may additionally or alternatively be monitored. Thedetected values of these physiological parameters of a patient 12 maydiscernibly change when the patient 12 falls asleep or wakes up. Some ofthese physiological parameters may be at low values when patient 12 isasleep. Further, the variability of at least some of these parameters,such as heart rate and respiration rate, may be at a low value when thepatient is asleep. Sensors 40 may be of any type known in the artcapable of generating a signal as a function of one or more of theseparameters.

For example, sensors 40 may include electrodes located on leads orintegrated as part of the housing of IMDs 14 that generate anelectrogram signal as a function of electrical activity of the heart ofa patient 12, and processor 46 may monitor the heart rate of the patient12 based on the electrogram signal. In other embodiments, a sensor mayinclude an acoustic sensor within IMDs 14, a pressure or flow sensorwithin the bloodstream or cerebrospinal fluid of a patient 12, or atemperature sensor located within the bloodstream of the patient 12. Thesignals generated by such sensors may vary as a function of contractionof the heart of a patient 12, and can be used by IMDs 14 to monitor theheart rate of a patient 12.

In some embodiments, processor 46 may detect, and measure values for oneor more ECG morphological features within an electrogram generated byelectrodes as described above. ECG morphological features may vary in amanner that indicates whether a patient 12 is asleep or awake. Forexample, the amplitude of the ST segment of the ECG may decrease when apatient 12 is asleep. Further, the amplitude of QRS complex or T-wavemay decrease, and the widths of the QRS complex and T-wave may increasewhen a patient 12 is asleep. The QT interval and the latency of anevoked response may increase when a patient 12 is asleep, and theamplitude of the evoked response may decrease when the patient 12 isasleep.

Sensors 40 may include one or more accelerometers, gyros, mercuryswitches, or bonded piezoelectric crystals that generate a signal as afunction of patient activity, e.g., body motion, footfalls or otherimpact events, and the like. Additionally or alternatively, sensors 40may include one or more electrodes that generate an electromyogram (EMG)signal as a function of muscle electrical activity, which may indicatethe activity level of a patient. The electrodes may be, for example,located in the legs, abdomen, chest, back or buttocks of a patient 12 todetect muscle activity associated with walking, running or the like. Theelectrodes may be coupled to IMDs 14 wirelessly or by leads 16 or, ifIMDs 14 are implanted in these locations, integrated with a housing ofIMDs 14.

However, bonded piezoelectric crystals located in these areas generatesignals as a function of muscle contraction in addition to body motion,footfalls or other impact events. Consequently, use of bondedpiezoelectric crystals to detect activity of a patient 12 may bepreferred in some embodiments in which it is desired to detect muscleactivity in addition to body motion, footfalls, or other impact events.Bonded piezoelectric crystals may be coupled to IMDs 14 wirelessly orvia leads 16, or piezoelectric crystals may be bonded to the can of IMDs14 when the IMDs are implanted in these areas, e.g., in the back,buttocks, chest, or abdomen of a patient 12.

Processor 46 may also detect spasmodic, irregular, movement disorder orpain related muscle activation via the signals generated by suchsensors. Such muscle activation may indicate that a patient 12 is notsleeping, e.g., unable to sleep, or if a patient 12 is sleeping, mayindicate a lower level of sleep quality.

Sensors 40 may also include a plurality of accelerometers, gyros, ormagnetometers oriented orthogonally that generate signals that indicatethe posture of a patient 12. In addition to being oriented orthogonallywith respect to each other, each of sensors 40 used to detect theposture of a patient 12 may be generally aligned with an axis of thebody of the patient 12. When accelerometers, for example, are aligned inthis manner, the magnitude and polarity of DC components of the signalsgenerate by the accelerometers indicate the orientation of the patientrelative to the Earth's gravity, e.g., the posture of a patient 12.Further information regarding use of orthogonally aligned accelerometersto determine patient posture may be found in a commonly-assigned U.S.Pat. No. 5,593,431, which issued to Todd J. Sheldon.

Other sensors 40 that may generate a signal that indicates the postureof a patient 12 include electrodes that generate an electromyogram (EMG)signal, or bonded piezoelectric crystals that generate a signal as afunction of contraction of muscles. Such sensors 40 may be implanted inthe legs, buttocks, chest, abdomen, or back of a patient 12, asdescribed above. The signals generated by such sensors when implanted inthese locations may vary based on the posture of a patient 12, e.g., mayvary based on whether the patient is standing, sitting, or lying down.

Further, the posture of a patient 12 may affect the thoracic impedanceof the patient. Consequently, sensors 40 may include an electrode pair,including one electrode integrated with the housing of IMDs 14 and oneof electrodes 42, that generates a signal as a function of the thoracicimpedance of a patient 12, and processor 46 may detect the posture orposture changes of the patient 12 based on the signal. The electrodes ofthe pair may be located on opposite sides of the patient's thorax. Forexample, the electrode pair may include one of electrodes 42 locatedproximate to the spine of a patient for delivery of SCS therapy, and IMD14 with an electrode integrated in its housing may be implanted in theabdomen of a patient 12.

Additionally, changes of the posture of a patient 12 may cause pressurechanges with the cerebrospinal fluid (CSF) of the patient. Consequently,sensors 40 may include pressure sensors coupled to one or moreintrathecal or intracerebroventricular catheters, or pressure sensorscoupled to IMDs 14 wirelessly or via leads 16. CSF pressure changesassociated with posture changes may be particularly evident within thebrain of the patient, e.g., may be particularly apparent in anintracranial pressure (ICP) waveform.

The thoracic impedance of a patient 12 may also vary based on therespiration of the patient 12. Consequently, in some embodiments, anelectrode pair that generates a signal as a function of the thoracicimpedance of a patient 12 may be used to detect respiration of thepatient 12. In other embodiments, sensors 40 may include a strain gauge,bonded piezoelectric element, or pressure sensor within the blood orcerebrospinal fluid that generates a signal that varies based on patientrespiration. An electrogram generated by electrodes as discussed abovemay also be modulated by patient respiration, and may be used as anindirect representation of respiration rate.

Sensors 40 may include electrodes that generate an electromyogram (EMG)signal as a function of muscle electrical activity, as described above,or may include any of a variety of known temperature sensors to generatea signal as a function of a core subcutaneous temperature of a patient12. Such electrodes and temperature sensors may be incorporated withinthe housing of IMDs 14, or coupled to IMDs 14 wirelessly or via leads.Sensors 40 may also include a pressure sensor within, or in contactwith, a blood vessel. The pressure sensor may generate a signal as afunction of the a blood pressure of a patient 12, and may, for example,comprise a Chronicle Hemodynamic Monitor™ commercially available fromMedtronic, Inc. of Minneapolis, Minn. Further, certain muscles of apatient 12, such as the muscles of the patient's neck, may discerniblyrelax when patient 12 is asleep or within certain sleep states.Consequently, sensors 40 may include strain gauges or EMG electrodesimplanted in such locations that generate a signal as a function ofmuscle tone.

Sensors 40 may also include optical pulse oximetry sensors or Clarkdissolved oxygen sensors located within, as part of a housing of, oroutside of IMDs 14, which generate signals as a function of blood oxygensaturation and blood oxygen partial pressure respectively. In someembodiments, systems 10 may include a catheter with a distal portionlocated within the cerebrospinal fluid of a patient 12, and the distalend may include a Clark sensor to generate a signal as a function of thepartial pressure of oxygen within the cerebrospinal fluid. Embodimentsin which an IMD comprises an implantable pump, for example, may includea catheter with a distal portion located in the CSF.

In some embodiments, sensors 40 may include one or more intraluminal,extraluminal, or external flow sensors positioned to generate a signalas a function of arterial blood flow. A flow sensor may be, for example,an electromagnetic, thermal convection, ultrasonic-Doppler, orlaser-Doppler flow sensor. Further, in some external medical deviceembodiments of the invention, sensors 40 may include one or moreelectrodes positioned on the skin of patient 12 to generate a signal asa function of galvanic skin response.

Additionally, in some embodiments, sensors 40 may include one or moreelectrodes positioned within or proximate to the brain of patient, whichdetect electrical activity of the brain. For example, in embodiments inwhich IMDs 14 delivers stimulation or other therapy to the brain,processor 46 may be coupled to electrodes implanted on or within thebrain via a leads 16. System 10B, illustrated in FIGS. 1B and 2B, is anexample of a system that includes electrodes 42, located on or withinthe brain of patient 12B, that are coupled to IMD 14B.

As shown in FIG. 2B, electrodes 42 may be selectively coupled to therapymodule 44 or an electroencephalogram (EEG) signal module 54 by amultiplexer 52, which operates under the control of processor 46. EEGsignal module 54 receives signals from a selected set of the electrodes42 via multiplexer 52 as controlled by processor 46. EEG signal module54 may analyze the EEG signal for certain features indicative of sleepor different sleep states, and provide indications of relating to sleepor sleep states to processor 46. Thus, electrodes 42 and EEG signalmodule 54 may be considered another sensor 40 in system 10B. IMD 14B mayinclude circuitry (not shown) that conditions the EEG signal such thatit may be analyzed by processor 52. For example, IMD 14B may include oneor more analog to digital converters to convert analog signals receivedfrom electrodes 42 into digital signals usable by processor 46, as wellas suitable filter and amplifier circuitry.

Processor 46 may also direct EEG signal module to analyze the EEG signalto determine whether patient 12B is sleeping, and such analysis may beconsidered alone or in combination with other physiological parametersto determine whether patient 12B is asleep. EEG signal module 60 mayprocess the EEG signals to detect when patient 12 is asleep using any ofa variety of techniques, such as techniques that identify whether apatient is asleep based on the amplitude and/or frequency of the EEGsignals. In some embodiments, the functionality of EEG signal module 54may be provided by processor 46, which, as described above, may includeone or more microprocessors, ASICs, or the like.

In other embodiments, processor 46 may be wirelessly coupled toelectrodes that detect brain electrical activity. For example, one ormore modules may be implanted beneath the scalp of the patient, eachmodule including a housing, one or more electrodes, and circuitry towirelessly transmit the signals detected by the one or more electrodesto IMDs 14. In other embodiments, the electrodes may be applied to thepatient's scalp, and electrically coupled to a module that includescircuitry for wirelessly transmitting the signals detected by theelectrodes to IMDs 14. The electrodes may be glued to the patient'sscalp, or a head band, hair net, cap, or the like may incorporate theelectrodes and the module, and may be worn by a patient 12 to apply theelectrodes to the patient's scalp when, for example, the patient isattempting to sleep. The signals detected by the electrodes andtransmitted to IMDs 14 may be EEG signals, and processor 46 may identifythe amplitude and or frequency of the EEG signals as physiologicalparameter values.

Also, the motion of the eyes of a patient 12 may vary depending onwhether the patient is sleeping and which sleep state the patient is in.Consequently, sensors 40 may include electrodes place proximate to theeyes of a patient 12 to detect electrical activity associated withmotion of the eyes, e.g., to generate an electro-oculography (EOG)signal. Such electrodes may be coupled to IMDs 14 via one or more leads16, or may be included within modules that include circuitry towirelessly transmit detected signals to IMDs 14. Wirelessly coupledmodules incorporating electrodes to detect eye motion may be wornexternally by a patient 12, e.g., attached to the skin of the patient 12proximate to the eyes by an adhesive when the patient is attempting tosleep.

Processor 46 may monitor one or more of these physiological parametersbased on the signals generated by the one or more sensors 40, anddetermine whether a patient 12 is attempting to sleep or asleep based oncurrent values for the physiological parameters. In some embodiments,processor 46 may determine mean or median value for the parameter basedon values of the signal over time, and determines whether a patient 12is asleep based on the mean or median value. In other embodiments,processor 46 may additionally or alternatively determine a variabilityof one or more of the parameters based on the values of the parameterover time, and may determine whether a patient 12 is asleep based on theone or more variability values. IMDs 14 may include circuitry (notshown) that conditions the signals generate by sensors 40 such that theymay be analyzed by processor 46. For example, IMDs 14 may include one ormore analog to digital converters to convert analog signals generate bysensors 40 into digital signals usable by processor 46, as well assuitable filter and amplifier circuitry.

In some embodiments, processor 46 determines a value of a sleep metricthat indicates a probability of the patient being asleep based on aphysiological parameter. In particular, processor 46 may apply afunction or look-up table to the current value, mean or median value,and/or variability of the physiological parameter to determine the sleepmetric value. For example, the values of one or more physiologicalparameters serve as indices to the lookup table to yield a correspondingoutput value, which serves as the sleep metric value. Processor 46 maycompare the sleep metric value to a threshold value to determine whethera patient 12 is asleep. In some embodiments, processor 46 may comparethe sleep metric value to each of a plurality of thresholds to determinethe current sleep state of a patient 12, e.g., rapid eye movement (REM),S1, S2, S3, or S4.

Further, in some embodiments processor 46 determines a sleep metricvalue for each of a plurality of monitored physiological parameters. Inother words, processor 46 may apply a function or look-up table for eachparameter to the current value for that parameter in order to determinethe sleep metric value for that individual parameter. Processor 46 maythen average or otherwise combine the plurality of sleep metric valuesto provide an overall sleep metric value, and may determine whether apatient 12 is asleep based on the overall sleep metric value. In someembodiments, processor 46 may apply a weighting factor to one or more ofthe sleep metric values prior to combination.

In some embodiments, the processor 46 may determine whether the patientis asleep, at least in part, by analyzing an electroencephalogram (EEG)of the patient. For example, the processor 46 may determine whether thepatient is asleep based on the amplitude or frequency, e.g., predominantfrequency, in the EEG. Further, the processor 46 may determine in whichsleep state (S1-S4 and REM) the patient is based on what frequency orrange of frequencies are evident in the EEG.

FIG. 3 is a logical diagram of an example circuit that detects whether apatient is asleep and/or the sleep type of a patient based on theelectroencephalogram (EEG) signal. As shown in FIG. 3, module 49 may beintegrated into an EEG signal module of IMDs 14 or a separateimplantable or external device capable of detecting an EEG signal. AnEEG signal detected by electrodes adjacent to the brain of a patent 12is transmitted into module 49 and provided to three channels, each ofwhich includes a respective one of amplifiers 51, 67 and 83, andbandpass filters 53, 69 and 85. In other embodiments, a common amplifieramplifies the EEG signal prior to filters 53, 69 and 85.

Bandpass filter 53 allows frequencies between approximately 4 Hz andapproximately 8 Hz, and signals within the frequency range may beprevalent in the EEG during S1 and S2 sleep states. Bandpass filter 69allows frequencies between approximately 1 Hz and approximately 3 Hz,which may be prevalent in the EEG during the S3 and S4 sleep states.Bandpass filter 85 allows frequencies between approximately 10 Hz andapproximately 50 Hz, which may be prevalent in the EEG during REM sleep.Each resulting signal may then processed to identify in which sleepstate a patient 12 is in.

After bandpass filtering of the original EEG signal, the filteredsignals are similarly processed in parallel before being delivered tosleep logic module 99. For ease of discussion, only one of the threechannels will be discussed herein, but each of the filtered signalswould be processed similarly.

Once the EEG signal is filtered by bandpass filter 53, the signal isrectified by full-wave rectifier 55. Modules 57 and 59 respectivelydetermine the foreground average and background average so that thecurrent energy level can be compared to a background level at comparator63. The signal from background average is increased by gain 61 beforebeing sent to comparator 63, because comparator 63 operates in the rangeof millivolts or volts while the EEG signal amplitude is originally onthe order of microvolts. The signal from comparator 63 is indicative ofsleep stages S1 and S2. If duration logic 65 determines that the signalis greater than a predetermined level for a predetermined amount oftime, the signal is sent to sleep logic module 99 indicating thatpatient 12 may be within the S1 or S2 sleep states. In some embodiments,as least duration logic 65, 81, 97 and sleep logic 99 may be embodied ina processor of the device containing EEG module 49.

Module 49 may detect all sleep types for a patient 12. Further, thebeginning of sleep may be detected by module 49 based on the sleep stateof a patient 12. Some of the components of module 49 may vary from theexample of FIG. 3. For example, gains 61, 77 and 93 may be provided fromthe same power source. Module 49 may be embodied as analog circuitry,digital circuitry, or a combination thereof.

In other embodiments, FIG. 3 may not need to reference the backgroundaverage to determine the current state of sleep of a patient 12.Instead, the power of the signals from bandpass filters 53, 69 and 85are compared to each other, and sleep logic module 99 determines whichthe sleep state of patient 12 based upon the frequency band that has thehighest power. In this case, the signals from full-wave rectifiers 55,71 and 87 are sent directly to a device that calculates the signalpower, such as a spectral power distribution module (PSD), and then tosleep logic module 99 which determines the frequency band of thegreatest power, e.g., the sleep state of a patient 12. In some cases,the signal from full-wave rectifiers 55, 71 and 87 may be normalized bya gain component to correctly weight each frequency band.

As shown in FIG. 4, memory 48 may include parameter information 60recorded by processor 46, e.g., parameter values, or mean or medianparameter values. Memory 48 may also store sleep metric functions 62 orlook-up tables (not shown) that processor 46 may retrieve forapplication to physiological parameter values or variability values, andthreshold values 64 that processor 46 may use to determine whether apatient 12 is asleep and, in some embodiments, the sleep state of apatient 12. Memory 48 may also store weighting factors 66 used byprocessor 46 when combining sleep metric values to determine an overallsleep metric value. Processor 46 may store sleep information 68 withinmemory 48, such as recorded sleep metric values and informationindicating when patient 12 was asleep.

As shown in FIGS. 2A and 2B, IMDs 14 also includes a telemetry circuit50 that allows processor 46 to communicate with clinician programmer 20and patient programmer 26. For example, using clinician programmer 20, aclinician may direct processor 46 to retrieve sleep information 68 frommemory 48 and transmit the information via telemetry circuit 50 toprogrammer 20 for analysis. Further, the clinician may select or adjustthe one or more of functions 62, look-up tables, thresholds 64 andweighting factors 66 in order to select or adjust the sensitivity andspecificity of processor 46 determining whether the patient is asleep.

FIG. 5 is a flowchart illustrating an example technique for determiningwhether a patient is asleep that may be employed by IMDs 14. Accordingto the example technique, IMDs 14 monitors a plurality of physiologicalparameters of a patient 12 (70). More particularly, processor 46receives signals from one or more sensors 40, and monitors thephysiological parameters based on the signals.

Processor 46 applies a respective function 62 to current values, mean ormedian values, and/or variability values for each of physiologicalparameters to determine a sleep metric value for each of the parameters(72). Processor 46 then combines the various sleep metric values todetermine a current overall sleep metric value (74). For exampleprocessor 46 may apply weighting factors 66 to one or more of theparameter specific sleep metric values, and then average the parameterspecific sleep metric values in light of the weighting factors 66.

Processor 46 compares the current overall sleep metric value to athreshold value 64 (76), and determines whether a patient 12 is asleepor awake, e.g., whether the sleep state of the patient 12 has changed,based on the comparison (78). For example, processor 46 may determinethat a patient 12 is asleep if the current overall sleep metric valueexceeds the threshold value 64. If the sleep state of a patient 12 haschanged, processor 46 may initiate, suspend or adjust a therapydelivered to the patient 12 by IMDs 14, or processor 46 may store anindication of the time and the change within memory 48 (80), e.g., foruse in evaluation of therapy or therapy parameter sets as describedabove.

FIG. 6 is a flow diagram illustrating an example method for collectingsleep quality information that may be employed by IMDs 14. In someembodiments, as discussed above, an IMD 14 may include sensors 40 thatdetect the posture and/or activity level of a patient 12. Furthermore,in some embodiments, IMDs 14 may include a sensor 40 that sensesmelatonin within one or more bodily fluids of the patients 12, such asthe patient's blood, cerebrospinal fluid (CSF), or interstitial fluid.IMDs 14 may also determine a melatonin level based on metabolites ofmelatonin located in the saliva or urine of the patient. Melatonin is ahormone secreted by the pineal gland into the bloodstream and the CSF asa function of exposure of the optic nerve to light, which synchronizesthe patient's circadian rhythm. In particular, increased levels ofmelatonin during evening hours may cause physiological changes in apatient 12, which, in turn, may cause the patient 12 to attempt to fallasleep.

An IMD 14 monitors the posture, activity level, and/or melatonin levelof a patient 12, or monitors for an indication from patient 12, e.g.,via patient programmer 26 (82), and determines whether patient 12 isattempting to fall asleep based on the posture, activity level,melatonin level, and/or a patient indication, as described above (84).IMDs 14 may, for example, detect an increase in the level of melatoninin a bodily fluid, and estimate the time that a patient 12 will attemptto fall asleep based on the detection. For example, IMDs 14 may comparethe melatonin level or rate of change in the melatonin level to athreshold level, and identify the time that threshold value is exceeded.IMDs 14 may identify the time that a patient 12 is attempting to fallasleep as the time that the threshold is exceeded, or some amount oftime after the threshold is exceeded.

If an IMD 14 determines that the patient 12 is attempting to fallasleep, the IMD 14 identifies the time that the patient 12 beganattempting to fall asleep (86), and monitors one or more of the variousphysiological parameters of the patient 12 discussed above to determinewhether the patient 12 is asleep (88, 90). For example, in someembodiments, the IMD 14 compares parameter values or parametervariability values to one or more threshold values 64 to determinewhether the patient 12 is asleep. In other embodiments, the IMD 14applies one or more functions or look-up tables to determine one or moresleep probability metric values based on the physiological parametervalues, and compares the sleep probability metric values to one or morethreshold values 64 to determine whether the patient 12 is asleep.Furthermore, in some embodiments an IMD 14 analyzes the amplitude and/orfrequency of an EEG signal to determine when the patient is asleep, asdescribed above with respect to FIG. 3. While monitoring physiologicalparameters (88) to determine whether patient 12 is asleep (90), the IMD14 may continue to monitor the posture and/or activity level of patient12 (82) to confirm that patient 12 is still attempting to fall asleep(84).

When the IMD 14 determines that the patient 12 is asleep, e.g., byanalysis of one or more of the various parameters contemplated herein,the IMD 14 may identify the time that the patient 12 fell asleep (92).While the patient 12 is sleeping, the IMD 14 will continue to monitorphysiological parameters of the patient 12 (94). As discussed above, theIMD 14 may identify the occurrence of arousals and/or apneas based onthe monitored physiological parameters (96). Further, the IMD 14 mayidentify the time that transitions between sleep states, e.g., REM, S1,S2, S3, and S4, occur (96). For example, the IMD 14 may compare one ormore sleep metric or physiological parameter values to one or morethresholds associated with the sleep states. As another example, the IMD14 may identify a sleep state based on the prominent frequency orfrequency range within an EEG of the patient, as described above withreference to FIG. 3.

Additionally, while the patient 12 is sleeping, the IMD 14 monitorsphysiological parameters of patient 12 (94) to determine whether patient12 has woken up (98). When the IMD 14 determines that the patient 12 isawake, the IMD 14 identifies the time that patient 12 awoke (100), anddetermines sleep quality metric values based on the informationcollected while the patient 12 was asleep (102).

For example, one sleep quality metric value an IMD 14 may calculate issleep efficiency, which the IMD 14 may calculate as a percentage of timeduring which a patient 12 is attempting to sleep that the patient 12 isactually asleep. An IMD 14 may determine a first amount of time betweenthe time the IMD 14 identified that the patient 12 fell asleep and thetime the IMD 14 identified that the patient 12 awoke. The IMD 14 mayalso determine a second amount of time between the time the IMD 14identified that the patient 12 began attempting to fall asleep and thetime the IMD 14 identified that the patient 12 awoke. To calculate thesleep efficiency, the IMD 14 may divide the first time by the secondtime.

Another sleep quality metric value that an IMD 14 may calculate is sleeplatency, which the IMD 14 may calculate as the amount of time betweenthe time the IMD 14 identified that the patient 12 was attempting tofall asleep and the time the IMD 14 identified that the patient 12 fellasleep. Other sleep quality metrics with values determined by an IMD 14based on the information collected by the IMD 14 in the illustratedexample include: total time sleeping per day, at night, and duringdaytime hours; number of apnea and arousal events per occurrence ofsleep; and amount of time spent in the various sleep states, e.g., oneor both of the S3 and S4 sleep states. An IMD 14 may store thedetermined values as sleep quality metric values 66 within memory 48.

An IMD 14 may perform the example method illustrated in FIG. 6continuously, e.g., may monitor to identify when patient 12 isattempting to sleep and asleep any time of day, each day. In otherembodiments, an IMD 14 may only perform the method during evening hoursand/or once every N days to conserve battery and memory resources.Further, in some embodiments, an IMD 14 may only perform the method inresponse to receiving a command from a patient 12 or a clinician via oneof programmers 20, 26. For example, a patient 12 may direct an IMD 14 tocollect sleep quality information at times when the patient believesthat his or her sleep quality is low or therapy is ineffective.

Sleep quality metric values determined by an IMD 14, e.g., using themethod of FIG. 6, may be provided to a clinician or other user via aprogrammer 20, 26 or other computing device. In some embodiments, theIMD 14 may associate sleep quality metric values with the therapy ortherapy parameter set in use when the values were determined. Suchembodiments may provide a list of therapy parameter sets and associatedsleep quality metric values to a user.

The invention is not limited to embodiments in which the therapydelivering medical device monitors the physiological parameters of thepatient described herein. In some embodiments, a separate monitoringdevice monitors values of one or more physiological parameters of thepatient instead of, or in addition to, a therapy delivering medicaldevice. The monitor may include a processor 46 and memory 48, and may becoupled to sensors 40, as illustrated above with reference to IMDs 14and FIGS. 2A, 2B and 3. The monitor may identify sleep and monitor sleepquality as described herein, or transmit physiological parameterinformation to another device, such as an IMD 14, programmer 20, 26, orother computing device for analysis of the signals to identify sleep ormonitor sleep quality. In some embodiments, an external computingdevice, such as a programming device, may incorporate the monitor.

FIG. 7 is a conceptual diagram illustrating a monitor that monitorsvalues of one or more accelerometers of the patient instead of, or inaddition to, such monitoring being performed by a therapy deliveringmedical device. As shown in FIG. 7, patient 12C is wearing monitor 104attached to belt 106. Monitor 104 is capable of receiving measurementsfrom one or more sensors located on or within patient 12C. In theexample of FIG. 7, accelerometers 108 and 110 are attached to the headand hand of patient 12C, respectively. Accelerometers 108 and 110 maymeasure movement of the extremities, or activity level, of patient 12Cto indicate when the patient moves during sleep or at other times duringthe day. Alternatively, more or less accelerometers or other sensors maybe used with monitor 104.

Accelerometers 108 and 110 may be preferably multi-axis accelerometers,but single-axis accelerometers may be used. As patient 12C moves,accelerometers 108 and 110 detect this movement and send the signals tomonitor 104. High frequency movements of patient 12C may be indicativeof tremor, Parkinson's disease, or an epileptic seizure, and monitor 104may be capable of indicating to IMDs 14, for example, that stimulationtherapy must be changed to effectively treat the patient. Accelerometers108 and 110 may be worn externally, i.e., on a piece or clothing or awatch, or implanted at specific locations within patient 12C. Inaddition, accelerometers 108 and 110 may transmit signals to monitor 104via wireless telemetry or a wired connection.

Monitor 82 may store the measurements from accelerometers 108 and 110 ina memory. In some examples, monitor 104 may transmit the measurementsfrom accelerometers 108 and 110 directly to another device, such as IMDs14, programming devices 20, 26, or other computing devices. In thiscase, the other device may analyze the measurements from accelerometers108 and 110 to detect efficacy of therapy or control the delivery oftherapy using any of the techniques described herein. In otherembodiments, monitor 104 may analyze the measurements using thetechniques described herein.

In some examples, a rolling window of time may be used when analyzingmeasurements from accelerometers 108 and 110. Absolute values determinedby accelerometers 108 and 110 may drift with time or the magnitude andfrequency of patient 12C movement may not be determined by a presetthreshold. For this reason, it may be advantageous to normalize andanalyze measurements from accelerometers 108 and 110 over a discretewindow of time. For example, the rolling window may be useful indetecting epileptic seizures. If monitor 104 or IMDs 14 detects at leasta predetermined number of movements over a 15 second window, anepileptic seizure may be most likely occurring. In this manner, a fewquick movements from patient 12C not associated with a seizure may nottrigger a response and change in therapy.

FIG. 8 is a flow diagram illustrating monitoring the heart rate andbreathing rate of a patient by measuring cerebral spinal fluid pressure.As discussed above, a physiological parameter that may be measured inpatient 12C is heart rate and respiration, or breathing, rate. In theexample of FIG. 8, cerebral spinal fluid (CSF) pressure may be analyzedto monitor the heart rate and breathing rate of patient 12C. A clinicianinitiates a CSF pressure sensor to being monitoring heart rate and/orbreathing rate (112). Alternatively, the CSF pressure sensor may beimplanted within the brain or spinal cord of patient 12C to acquireaccurate pressure signals. The CSF pressure sensor must also store thepressure data or begin to transfer pressure data to an implanted orexternal device. As an example used herein, the CSF pressure sensortransmits signal data to an IMD 14.

Once the CSF pressure sensor is initiated, the CSF pressure sensormeasures CSF pressure and transmits the data to IMD 14 (114). The IMD 14analyzes the CSF pressure signal to identify the heart rate (116) andbreathing rate (118) of patient 12C. The heart rate and breathing ratecan be identified within the overall CSF pressure signal. Higherfrequency fluctuations (e.g. 40 to 150 beats per minute) can beidentified as the heart rate while lower frequency fluctuations (e.g. 3to 20 breaths per minute) in CSF pressure are the breathing rate. An IMD14 may employ filters, transformations, or other signal processingtechniques to identify the heart rate and breathing rate from the CSFpressure signal. IMDs 14 may utilize the heart rate and breathing rateinformation as additional information when determining the sleep metricof patient 12C (120).

Various embodiments of the invention have been described. However, oneskilled in the art will appreciated that various modifications may bemade to the described embodiments without departing from the scope ofthe invention. For example, although described herein in the context ofan implantable neurostimulator, the invention may be embodied in anyimplantable or external device. Further, the invention may be embodiedin devices that treat any a variety of disorders of the patient.

As discussed above, the ability of a patient to experience qualitysleep, e.g., the extent to which the patient able to achieve adequateperiods of undisturbed sleep in deeper, more restful sleep states, maybe negatively impacted by any of a variety of ailments or symptoms.Accordingly, the sleep patterns or sleep quality of a patient mayreflect the progression, status, or severity of the ailment or symptom.Further, the sleep patterns or quality of the patient may reflect theefficacy of a particular therapy or therapy parameter set in treatingthe ailment or symptom. In other words, it may generally be the casethat the more efficacious a therapy or therapy parameter set is, thehigher quality of sleep the patient will experience.

As discussed above, in accordance with the invention, systems may usethe sleep detection techniques of the invention to monitor sleep qualityor sleep patterns, which may be used to evaluate the status, progressionor severity of an ailment or symptom, or the efficacy of therapies ortherapy parameter sets used to treat the ailment or symptom. As anexample, chronic pain may cause a patient to have difficulty fallingasleep, experience arousals during sleep, or have difficultyexperiencing deeper sleep states. Systems according to the invention maymonitor sleep to evaluate the extent to which the patient isexperiencing pain.

In some embodiments, systems according to the invention may include anyof a variety of medical devices that deliver any of a variety oftherapies to treat chronic pain, such as SCS, DBS, cranial nervestimulation, peripheral nerve stimulation, or one or more drugs. Systemsmay use the techniques of the invention described above to determinewhen the patient is asleep or in certain sleep states, monitor sleeppatterns based on such sleep state information, and thereby facilitateevaluation of any of the above-identified therapies. Systems accordingto the invention may thereby evaluate the extent to which a therapy ortherapy parameter set is alleviating chronic pain by evaluating theextent to which the therapy or therapy parameter set improves sleepquality or patterns for the patient.

As another example, psychological disorders may cause a patient toexperience low sleep quality. Accordingly, embodiments of the inventionmay monitor sleep or sleep states that sleep quality to track the statusor progression of a psychological disorder, such as depression, mania,bipolar disorder, or obsessive-compulsive disorder. Further, systemsaccording to the invention may include any of a variety of medicaldevices that deliver any of a variety of therapies to treat apsychological disorder, such as DBS, cranial nerve stimulation,peripheral nerve stimulation, vagal nerve stimulation, or one or moredrugs. Systems may use the techniques of the invention described aboveto associate sleep pattern or quality information with the therapies ortherapy parameter sets for delivery of such therapies, and therebyevaluate the extent to which a therapy or therapy parameter set isalleviating the psychological disorder by evaluating the extent to whichthe therapy parameter set improves the sleep quality of the patient.

Movement disorders, such as tremor, Parkinson's disease, multiplesclerosis, spasticity, or epilepsy, may also affect sleep patterns andthe sleep quality experienced by a patient. The uncontrolled movements,e.g., tremor or shaking, associated such disorders, particularly in thelimbs, may cause a patient to experience disturbed sleep. Accordingly,systems according to the invention may monitor sleep, sleep states,sleep patterns, or sleep quality of the patient to determine the stateor progression of a movement disorder. Both psychological disorders andmovement disorders are examples of neurological disorders that mayafflict a patient 12.

Further, systems according to the invention may include any of a varietyof medical devices that deliver any of a variety of therapies to treat amovement disorders, such as DBS, cortical stimulation, or one or moredrugs. Baclofen, which may or may not be intrathecally delivered, is anexample of a drug that may be delivered to treat movement disorders.Systems may use the techniques of the invention described above toassociate sleep pattern or quality information with therapies or therapyparameter sets for delivery of such therapies. In this manner, suchsystems may allow a user to evaluate the extent to which a therapy ortherapy parameter set is alleviating the movement disorder by evaluatingthe extent to which the therapy parameter set improves the sleep qualityexperienced by the patient.

As another example, although described in the context of determiningwhether a patient is asleep, e.g., whether the patient's current sleepstate is asleep or awake, the invention may, as described above, be usedto determine what level of sleep a patient is currently experiencing,e.g., which of sleep states REM, S1, S2, S3, and S4 the patient iscurrently in. A medical device may record transitions between thesestates and between sleep and wakefulness, or may control therapy basedon transitions between these states and between sleep and wakefulness.Further, in some embodiments, a medical device may, without making asleep determination, simply record one or more determined sleep metricvalues for later analysis, or may control delivery of therapy based onthe sleep metric values.

Further, the invention may be embodied in a programming device, such asprogrammers 20, 26 described above, or another type of computing device.In particular, in some embodiments, a computing device may determinewhen a patient 12 is asleep according to the invention instead of, or inaddition to an implantable or external medical device. For example, amedical device may record values for one or more of the physiologicalparameters discussed herein, and may provide the physiological parametervalues to the computing device in real time or when interrogated by thecomputing device. The computing device may apply the techniquesdescribed herein with reference to IMDs 14 to determine when a patient12 is asleep, and may control delivery of therapy based on thedetermination, or present information relating to the patient's sleeppatterns to a user to enable diagnosis or therapy evaluation. Thecomputing device may be a programming device, such as programmers 20,26, or may be any handheld computer, desktop computer, workstation, orserver. A computing device, such as a server, may receive informationfrom the medical device and present information to a user via a network,such as a local area network (LAN), wide area network (WAN), or theInternet.

The invention may also be embodied as a computer-readable medium, suchas memory 48, that includes instructions to cause a processor, such asprocessor 46, to perform any of the methods described herein. These andother embodiments are within the scope of the following claims.

1. A method for evaluating the efficacy of at least one of a movementdisorder therapy, psychological disorder therapy, or deep brainstimulation comprising: monitoring at least physiological parameter of apatient via an implantable medical device that delivers the at least oneof the movement disorder therapy, psychological disorder therapy, ordeep brain stimulation to the patient; monitoring sleep patterns of thepatient with the implantable medical device based on the physiologicalparameter; and presenting sleep quality information to a user based onthe sleep patterns for evaluation of the efficacy of the at least one ofthe movement disorder therapy, psychological disorder therapy, or deepbrain stimulation.
 2. The method of claim 1, wherein monitoring sleeppatterns comprises: determining the value of a sleep probability metricbased on the physiological parameter; and determining whether thepatient is asleep based on the sleep probability metric.
 3. The methodof claim 2, wherein monitoring at least physiological parametercomprises monitoring a plurality of physiological parameters, whereindetermining a value of a sleep probability metric comprises determininga value for each of a plurality of sleep probability metrics, each ofthe values determined based on a respective one of the physiologicalparameters, and wherein determining whether the patient is asleepcomprises determining whether the patient is asleep based on theplurality of sleep probability metrics.
 4. The method of claim 3,wherein determining whether the patient is asleep based on the pluralityof sleep probability metrics comprises: applying a weighting value to atleast one of the sleep probability metrics; and determining a value ofan overall sleep probability metric based on the plurality of sleepprobability metric values.
 5. The method of claim 2, further comprisingcomparing the value of the sleep probability metric to a plurality ofthresholds, wherein monitoring sleep patterns of the patient comprisesdetermining a sleep state of the patient based on the comparison.
 6. Themethod of claim 5, wherein determining a sleep state of the patientcomprises determining whether the patient is in one of an S3 or and S4sleep state.
 7. The method of claim 1, wherein monitoring at leastphysiological parameter of a patient comprising monitoring anelectroencephalogram (EEG) of the patient, and wherein monitoring sleeppatterns of the patient comprises determining a sleep state of thepatient based on a frequency of the EEG.
 8. The method of claim 7,wherein determining a sleep state of the patient comprises determiningwhether the patient is in one of an S3 or and S4 sleep state.
 9. Amedical system comprising: a sensor that generates a signal as afunction of at least one physiological parameter of a patient; animplantable medical device that delivers at least one of a movementdisorder therapy, psychological disorder therapy, or deep brainstimulation, monitors the at least one physiological parameter of thepatient based on the signal output by the sensor, and monitors sleeppatterns of the patient based on the physiological parameter; and acomputing device that provides sleep quality information based on thesleep patterns for evaluation of the efficacy of the at least one of themovement disorder therapy, psychological disorder therapy, or deep brainstimulation.
 10. The medical system of claim 9, wherein the implantablemedical device determines the value of a sleep probability metric basedon the physiological parameter, and determines whether the patient isasleep based on the sleep probability metric.
 11. The medical system ofclaim 10, further comprising a plurality of sensors, each of the sensorsgenerating a respective signal as a function of at least one of aplurality of physiological parameters, wherein the implantable medicaldevice monitors the plurality of physiological parameters, determines avalue for each of a plurality of sleep probability metrics, each of thevalues determined based on a respective one of the physiologicalparameters, and determines whether the patient is asleep based on theplurality of sleep probability metrics.
 12. The medical system of claim11, wherein the implantable medical device applies a weighting value toat least one of the sleep probability metrics, and determines a value ofan overall sleep probability metric based on the plurality of sleepprobability metric values.
 13. The medical system of claim 10, whereinthe implantable medical device compares the probability metric to aplurality of thresholds, and monitors sleep patterns by determining asleep state of the patient based on the comparison.
 14. The medicalsystem of claim 13, wherein the implantable medical device determineswhether the patient is in one of an S3 or and S4 sleep state.
 15. Themedical system of claim 9, wherein the sensor comprises an electrodecoupled to the implantable medical device, and wherein the implantablemedical device monitors an electroencephalogram (EEG) of the patient viathe electrode, and monitors sleep patterns of the patient by determininga sleep state of the patient based on a frequency of the EEG.
 16. Themedical system of claim 15, wherein the implantable medical devicedetermines whether the patient is in one of an S3 or and S4 sleep state.17. The medical system of claim 9, wherein the implantable medicaldevice comprises at least one of an implantable neurostimulator or animplantable pump.
 18. A medical system comprising: means for monitoringat least physiological parameter of a patient via an implantable medicaldevice that delivers the at least one of the movement disorder therapy,psychological disorder therapy, or deep brain stimulation to thepatient; means for monitoring sleep patterns of the patient with theimplantable medical device based on the physiological parameter; andmeans for presenting sleep quality information to a user based on thesleep patterns for evaluation of the efficacy of the at least one of themovement disorder therapy, psychological disorder therapy, or deep brainstimulation.
 19. The medical system of claim 18, wherein the means formonitoring sleep patterns comprises: means for determining the value ofa sleep probability metric based on the physiological parameter; andmeans for determining whether the patient is asleep based on the sleepprobability metric.
 20. The medical system of claim 18, wherein themeans for monitoring sleep patterns comprises means for identifyingwhether that patient is in at least one of a S1, S2, S3, S4 or rapid eyemovement (REM) sleep state.